The following account of the prior art relates to one of the areas of application of the present invention, hearing aids.
It is well-known that in standard adaptive feedback cancellation systems, correlation between the receiver signal and the microphone target signal, the so-called autocorrelation (AC) problem, leads to a biased estimate of the feedback transfer function. This, in turn, leads to cancellation of (parts of) the target signal and/or sub-oscillation/howls due to bias in the estimate of the feedback transfer function. One way to deal with the AC problem is to rely on AC detectors and decrease convergence rate in sub-bands where AC is dominant, see e.g. WO 2007/113282 A1 (Widex). Although this is definitely better than not dealing with the AC problem at all, the disadvantage is that adaptation can be very slow in frequency regions often dominated by AC, e.g. low-frequency regions in speech signals. Another way to deal with the AC problem is to introduce so-called probe noise, where an, ideally inaudible, noise sequence is combined with the receiver signal before play back (being presented to a user). In principle, this well-known class of methods, see e.g. EP 0 415 677 A2 (G N Danavox), completely eliminates the AC problem. However, since in general the probe noise variance must be very small for the noise to be inaudible, the resulting adaptive system becomes very slow. An improvement can be obtained by using masked noise as e.g. described in US 2007/172080 A1 (Philips).
WO 2007/125132 A2 (Phonak) describes a method for cancelling or preventing feedback. The method comprises the steps of estimating an external transfer function of an external feedback path defined by sound travelling from the receiver to the microphone, estimating the input signal having no feedback components of the external feedback path using an auxiliary signal, which does not comprise feedback components of the external feedback path, and using the estimated input signal for estimating the external transfer function of the external feedback path.
Traditional Probe Noise Solution:
Prior art probe noise based solutions of an adaptive feedback cancellation (FBC) system, where, ideally, a perceptually undetectable noise sequence is added to the receiver signal, can in principle completely by-pass the AC-problem. FIG. 1a shows an example of an audio processing system, e.g. a listening device, comprising a traditional adaptive system based on probe noise, where the goal is to approximate the unknown, time-varying transfer function F(z,n) (representing leakage feedback from receiver to microphone) by an estimate Fh(z,n), which here is assumed to be an FIR system. A forward path is defined between the microphone and the receiver. The estimate Fh(z,n) may be updated using any of the standard adaptive filtering algorithms such as NLMS, RLS, etc. (cf. Algorithm unit feeding update filter coefficients to variable filter part Fh(z,n) in FIG. 1a). The probe noise (generated by Probe signal unit in FIG. 1a) is denoted as us(n) and can be generated in a variety of ways (cf. e.g. methods A and B discussed below or any other appropriate method, e.g. by filtering a white noise sequence through an analysis-modification-synthesis filter bank, or through an IIR filter). The probe signal us(n) is connected to the Algorithm part of the adaptive FBC-filter as well as being added to output signal y(n) from the forward gain unit G(z,n) in output SUM unit ‘+’, whose output u(n) is connected to the receiver and to the variable filter part Fh(z,n) of the adaptive FBC-filter. The Algorithm part additionally bases the estimate of filter coefficients of the variable filter part Fh(z,n) of the FBC-filter on the feedback corrected input signal e(n) generated by a subtraction in input SUM unit ‘+’ of the feedback estimate vh(n) of the variable filter part Fh(z,n) of the FBC-filter from the input signal comprising feedback signal v(n) and target signal x(n) as picked up by the microphone. Due to the preferably inaudible nature of the probe noise, such prior art solutions lead to relatively slow adaption rates of the adaptive system.